A poster presented at
The Conference on Scientific and Technical Data
Exchange and Integration
Sponsored by U.S. National Committee for CODATA
National Research Council
December 15-17, 1997
Natcher Conference Center
National Institute of Health
Bethesda, MD
E-mail for authors:
nystuen@umich.edu
jambo@umich.edu
Abstract
Map databases are integral to many ITS (Intelligent
Transportation Systems) applications in navigation, traffic forecasting,
and route planning. With the increasing deployment of ITS technology demands
for accurate and complete digital map databases of the nation's road network
are surging. The development and maintenance of high quality digital map
databases is expensive and time-consuming. Database sharing will be a sensible
approach whenever possible in order to reduce cost. In the US map databases
are being produced by a variety of public agencies and private vendors.
Quality and levels of accuracy vary depending on data sources and production
procedures. Verifying the quality and accuracy of map databases for purposes
of navigation is a pragmatic and important concern. The Society of Automotive
Engineers (SAE) has developed a Truth-in-Labeling Standard (SAE document
J1663), the goal of which is to provide a consistent method for describing
and comparing map databases. While the standard requires that database
vendors provide a standardized label that lists basic database characteristics
such as lineage, coverage, accuracy, content and scope of a database, there
are currently no guidelines for feature representation (such as the layout
of road intersections) in digital databases. Comparison of two different
map databases reveals significant representational differences due to differences
in precision of source material, data model and intended uses.
Problem
Current standards for data exchange are insufficient
for unambiguous and successful transfer of information between digital
map databases, in part because of semantic differences in feature representation.
Real world entities are complex. Which faces of this complexity are captured
in the feature representation depend upon broader contexts and circumstances
than can be reported in the metadata statements about the database. The
examples presented here illustrate the dimensions of this problem.
Keypoints
Various reasons for the different representation
of features in digital map databases exist. Four main reasons/sources of
representational differences are distinguished. They are illustrated and
discussed below.
1. Differences in Feature representation due
to different interests. Agencies and companies focus on different types
of geographic features. Municipal governments often take an area-oriented
perspective, representing parcels and streets as polygons. Vendors of digital
navigation maps use a network view with street addresses, street names
and driver instructions.
Figure 1a
Figure 1b
2. Differences in feature representation due
to underlying data model. There are many public and private sources for
digital map data. Agencies and companies develop their own data models
that may be proprietary. "Stone School Rd" crossing the Interstate shows
that one database uses a planar model (black arclines), whereas the other
employs a non-planar model (blue arclines, no nodes).
Figure 2
3. Differences in feature representation due to individual preferences. Operators who digitize maps may develop individual ways to represent features such as road intersections or dead end streets.
Figure3a
Figure3b
Figure3c
Figure3d
4. Difference in feature representation due to map
scale and resolution. The larger the scale and finer the resolution of
the original map, the more detail can be expected in features such as intersections.
Figure 4
Discussion
One means to address standardization of feature
representation is to provide detailed descriptions of the data set through
metadata. While this is attempted in the new Truth-in-Labeling standard,
it does not address feature representation. Metadata requirements are new.
The Truth-in-labeling approach for standards applies to new datasets. Metadata
for older dataset are hard to reconstruct; time depth and therefore change
data simply may not be available. While standards provide a good start
- the descriptive requirements may not be of sufficient detail for meaningful
data sharing. Subtle data modeling differences create not so subtle differences
in feature representations. Transportation data models may be planar or
non-planar. An overpass of one road over another road in a planar model
is generally represented by a node with four incident arcs. The node at
the intersection has an associated attribute describing turn restrictions
in order to convey the correct driver instructions (i.e. for route guidance).
In a non-planar model of the same feature - no node exist since the arcs
are unconnected in the 3-dimensional space. Thus, linking non-planar and
planar databases is problematic.
Conclusion
The consequences of different feature representation
are manifold. In terms of data exchange, data sharing and integration different
feature representation leads to
- Lack of comparability (pattern matching)
- Lack of compatibility (data base/model anomalies)
Furthermore results from data analyses performed on different databases are likely to display different results. Differences in feature representation become an issue with increasing interest and need to exchange data. Generally there are two approaches to overcome the problem. 1. Descriptive and detailed metadata provision that includes information on feature representation is used to emphasize the different treatment of the features. 2. Standardization or formulation of conventions for feature representation which consider semantic differences.
Acknowledgments
This research was made possible by a grant from Federal Highway Administration (DOT). We would like to further acknowledge the generous provision of digital map databases and orthophotographs by the City of Ann Arbor, MI; ETAK Corp., Southeastern Michigan Council of Governments (SEMCOG), and NavTech Inc.
References
Nystuen, John D., Andrea I. Frank and Larry Frank,
jr. "Assessing Topological Similarity of Spatial Networks." Paper presented
at The International conference on Interoperating Geographic Information
Systems INTEROP 97. Sponsored by The National Center for Geographic Infromation
and analysis (NCGIA). December 3 – 4, 1997. Santa Barbara, CA – USA